Extracting Fuzzy Rules Under Uncertainty and Measuring Definability Using Rough Sets

Article type: Research Article

Authors: de Korvin, A. | Bourgeois, B. | Kleyle, R.

Affiliations: Department of Applied Mathematical Sciences, University of Houston–Downtown, Houston, Texas 77002 | Department of Electrical Engineering, University of Houston, Houston, Texas 77204-4793 | Department of Mathematical Sciences, Indiana University-Purdue, University of Indianapolis, Indianapolis, Indiana 46205

Abstract: In this article we consider attributes to be fuzzy sets. Knowledge acquisition takes place by looking at examples. In each example, attributes as well as a corresponding decision is made available. The decision may be a fuzzy diagnosis. Based on these examples two sets of fuzzy rules are constructed: certain rules and possible rules. Corresponding measures of how much we believe these rules are also constructed. The concept of how much a fuzzy diagnosis is definable in terms of fuzzy attributes is studied. Finally, classifications and some of their properties are analyzed.

DOI: 10.3233/IFS-1994-2106

Journal: Journal of Intelligent and Fuzzy Systems, vol. 2, no. 1, pp. 75-87, 1994